Determining and understanding how individuals navigate between places on foot is important to many aspects of society. City planners, civil engineers, marketing companies, business owners, forest rangers, and so on are all interested in understanding travel patterns so that they can understand the needs of those they serve and improve the services they provide. For example, a city planner may determine that various pathways or walkways need to be improved based on detected or known travel patterns. As another example, a forest ranger may determine that a particular trail should be closed or highlighted if the trail is used infrequently. As another example, a marketing company made decide where to place (or not place) advertisements or other marketing materials based on travel patterns.
Present data collection approaches for foot travel include surveys, manual counts, stationary sensors (infra-red trip line sensors or inductance loops), and video-based data collection system. With the exception of widely-distributed surveys, these approaches provide data for a very limited region (e.g., a single data collection point) or require infrastructure that can be expensive to install and maintain. Similarly, widely-distributed surveys can be expensive to disseminate and process and may suffer from self-selection bias as some individuals simply will not take the time to complete surveys. Furthermore, some communities choose to conduct surveys or travel pattern studies infrequently (due to costs), such as once per year. This approach does not provide adequate travel pattern data as it does not account for changes in travel patterns throughout the year or even over the course of a day. Current techniques for determining and understanding pedestrian travel patterns are typically expensive to install and maintain, do not scale well, and do not provide sufficient data to monitor a wide area over a wide timeframe.